Algorithmic Short Selling with Python E-Book

Laurent Bernut

• Herausgeber: Packt Publishing
• Kategorie: Fachliteratur
• Sprache: Englisch
• Veröffentlichungsjahr: 2021

Seitenzahl: 531

Beschreibung

If you are in the long/short business, learning how to sell short is not a choice. Short selling is the key to raising assets under management. This book will help you demystify and hone the short selling craft, providing Python source code to construct a robust long/short portfolio. It discusses fundamental and advanced trading concepts from the perspective of a veteran short seller.

This book will take you on a journey from an idea (“buy bullish stocks, sell bearish ones”) to becoming part of the elite club of long/short hedge fund algorithmic traders. You’ll explore key concepts such as trading psychology, trading edge, regime definition, signal processing, position sizing, risk management, and asset allocation, one obstacle at a time. Along the way, you’ll will discover simple methods to consistently generate investment ideas, and consider variables that impact returns, volatility, and overall attractiveness of returns.

By the end of this book, you’ll not only become familiar with some of the most sophisticated concepts in capital markets, but also have Python source code to construct a long/short product that investors are bound to find attractive.

Leseprobe

Algorithmic Short Selling with Python

Refine your algorithmic trading edge, consistently generate investment ideas, and build a robust long/short product

Laurent Bernut

BIRMINGHAM—MUMBAI

"Python" and the Python Logo are trademarks of the Python Software Foundation.

Algorithmic Short Selling with Python

Copyright © 2021 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Producer: Dr. Shailesh Jain

Acquisition Editor – Peer Reviews: Saby D'silva

Project Editor: Rianna Rodrigues

Development Editor: Edward Doxey

Copy Editor: Safis Editing

Technical Editor: Aditya Sawant

Proofreader: Safis Editing

Indexer: Manju Arasan

Presentation Designer: Pranit Padwal

First published: September 2021

Production reference: 291221

Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK.

ISBN 978-1-80181-519-2

www.packt.com

Foreword

Since 1996 I have actively and publicly advised investors in the use and execution of a long/short trading strategy well known as trend following. Long/short. That's two words. But amazingly, after all these years, too many investors and traders miss out on the short side of what I do. They see the word short. They perhaps even know what it means, but they stay fixated on the long side. Bull markets drive the emotional, visceral side of us human beings, and people can't get enough of the idea that they could be on board with the next Apple, Tesla, Amazon, or Bitcoin at some cheap price. In theory, instant riches as the next one goes to the moon!

This lack of focus on the short side is rather odd. We all saw The Big Short. Michael Burry shot to massive fame through that spectacular short trade. We all admired his call during that chaotic period of time. So we all know that epic up moves often have epic down moves. Moonshots don't stay at the moon. They can crash to hell. Do your quick boom bust market history. Scan the charts. My views are not breaking new ground. But where is The Big Short II? Was Burry just lucky on that one particular short trade? Doubtful, but I'm not sure, and nor are you, I imagine. That said, I want repeatability. I want a strategy that can survive for decades.

My life first intersected with a dedicated short seller in 2012 (not too long after Burry's infamous trade), when Laurent Bernut, a short trader based in Tokyo, reached out to me with a speaking opportunity in Tokyo. I took that opportunity and it quickly expanded into a speaking tour across Asia. Twenty cities, ten countries, and I was hooked on Asia. I have lived there since. Cheers to Laurent for being a part of chaos theory! Now, you might not think that this professional relationship was a match. I was coming at trading from a systematic trend following perspective and Laurent was coming at trading through a systematic short perspective. The key word though is systematic. And a systematic approach is aimed at one thing: repeatability.

Let me go off on a tangent for a moment. When legendary trading author Jack Schwager recently came on my podcast to promote his new book, it was a no brainer for me to have him on. Now, even though Jack knows I have a trend following bias, he also knows I have an open mind. I knew that when he wrote about a series of unknown traders, he had vetted them and their strategies thoroughly. The common denominator, just like with Laurent and I, was a fixation on systematic and repeatable strategies.

This brings me back to Laurent's new book, Algorithmic Short Selling with Python. In it, Laurent conveys a determined passion for the short side that has driven him for over a decade. He sees the opportunity, and knows the value in a systematic short side bias. His book, the one you're reading right now, brings excruciating detail and the transparency traders will need to excel on the short side as well as the long side.

From choosing to enter or exit a position, to managing risk and position size, to visualizing and managing a whole portfolio, Laurent breaks down the process and psychology behind developing an effective long/short trading strategy. He also includes versatile source code in Python, which can be implemented on any stock, in any market, with the sole intention of limiting loss, and yielding above average returns. That's all you can ask for from a teacher. Show me all you know. Give it to me straight and hold nothing back.

Now it's up to you to take Laurent's hard work and go apply it.

Michael Covel

Author of Trend Following and TurtleTrader

Contributors

About the author

Laurent Bernut has 2 decades of experience in alternative investment space. After the US CPA, he compiled financial statements in Japanese and English for a Tokyo Stock Exchange-listed corporation. After serving as an analyst in two Tokyo-based hedge funds, he joined Fidelity Investments Japan as a dedicated quantitative short-seller. Laurent has built numerous portfolio management systems and developed several quantitative models across various platforms. He currently writes and runs algorithmic strategies and is an undisputed authority on short selling on Quora, where he was nominated top writer for 2017, 2018, and 2019.

John McLaughlin, your authentic mentorship manifested this book. Michael Covel, thank you my friend. Grand merci to my editors Ed Doxey and Sofien Kaabar, and to Dr. Shailesh Jain, who believed in this project. Scott Phillips, I love you. Sincere gratitude to Franklin Parker, Douglas Marsh, Andrew Swanscott from the Better System Trader podcast, Alex Ribeiro Castro, Nitesh Khandelwal, and Wazir Kahar. Thank you also to Victor Haghani. Last, but first in my heart, thank you to Jules and Alizee for your loving inspiration.

About the reviewer

Sofien Kaabar is an institutional market strategist with a focus on technical and quantitative strategies. Having graduated from SKEMA business school in Paris and with a background in trading and research, he is currently focused on trading automation and strategies.

I would like to thank my family, especially my parents, who are always on the front line for me and my two sisters, as well as my fiancée, Charline, who tolerates my excessive working time.

Contents

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Share your thoughts

The Stock Market Game

Is the stock market art or science?

How do you win this complex, infinite, random game?

How do you win an infinite game?

How do you beat complexity?

How do you beat randomness?

Playing the short selling game

Summary

10 Classic Myths About Short Selling

Myth #1: Short sellers destroy pensions

Myth #2: Short sellers destroy companies

Myth #3: Short sellers destroy value

Myth #4: Short sellers are evil speculators

Myth #5: Short selling has unlimited loss potential but limited profit potential

Myth #6: Short selling increases risk

Myth #7: Short selling increases market volatility

Myth #8: Short selling collapses share prices

Myth #9: Short selling is unnecessary during bull markets

Myth #10: The myth of the "structural short"

Summary

Take a Walk on the Wild Short Side

The long side world according to GARP

Structural shorts: the unicorns of the financial services industry

Overcoming learned helplessness

Money "is" made between events that "should" happen

The unique challenges of the short side

Market dynamics: short selling is not a stock-picking contest, but a position-sizing exercise

Scarcity mentality

Asymmetry of information

Stock options and transparency

Sell-side analysts are the guardians of the financial galaxy

Summary

Long/Short Methodologies: Absolute and Relative

Importing libraries

Long/Short 1.0: the absolute method

Ineffective at decreasing correlation with the benchmark

Ineffective at reducing volatility

Little, if any, historical downside protection

Lesser investment vehicle

Laggard indicator

Long/Short 2.0: the relative weakness method

Consistent supply of fresh ideas on both sides

Focus on sector rotation

Provides a low-correlation product

Provides a low-volatility product

Reduces the cost of borrow fees

Provides scalability

Non-confrontational

Currency adjustment becomes an advantage

Other market participants cannot guess your levels

You will look like an investment genius

Summary

Regime Definition

Importing libraries

Creating a charting function

Breakout/breakdown

Moving average crossover

Higher highs/higher lows

The floor/ceiling method

Swing detection

Historical swings and high/low alternation

Establishing trend exhaustion

Putting it all together: regime detection

Regime definition

Methodology comparison

Timing the optimal entry point after the bottom or the peak

Seeing through the fundamental news flow

Recognizing turning points

Let the market regime dictate the best strategy

Summary

The Trading Edge is a Number, and Here is the Formula

Importing libraries

The trading edge formula

Technological edge

Information edge

Statistical edge

A trading edge is not a story

Signal module: entries and exits

Entries: stock picking is vastly overrated

Exits: the transmutation of paper profits into real money

Regardless of the asset class, there are only two strategies

Trend following

Mean reversion

Summary

Improve Your Trading Edge

Blending trading styles

The psychology of the stop loss

Step 1: Accountability

Step 2: Rewire your association with losses

Step 3: When to set a stop loss

Step 4: Pre-mortem: the vaccine against overconfidence

Step 5: Executing stop losses: forgiving ourselves for mistakes

Step 6: What the Zeigarnik effect can teach us about executing stop losses

The science of the stop loss

Stop losses are a logical signal-to-noise issue

Stop losses are a statistical issue

Stop losses are a budgetary issue

Techniques to improve your trading edge

Technique 1: The game of two halves: how to cut losers, ride winners, and maintain conviction while improving your trading edge

Technique 2: Mitigate losses with a trailing stop

Technique 3: the game of two-thirds: time exit and how to trim freeloaders

Technique 4: The profit side: reduce risk and compound returns by taking small profits

Technique 5: Elongate the right tail

Technique 6: Re-entry: Ride your winners by laddering your positions

Final exit: the right tail

Re-entry after a final exit

How to tilt your trading edge if your dominant style is mean reversion

Losses

Profits

Partial exit

Exits

Summary

Position Sizing: Money is Made in the Money Management Module

Importing libraries

The four horsemen of apocalyptic position sizing

Horseman 1: Liquidity is the currency of bear markets

Horseman 2: Averaging down

Horseman 3: High conviction

Horseman 4: Equal weight

Position sizing is the link between emotional and financial capital

A position size your brain can trade

Establishing risk bands

Equity curve oscillator – avoiding the binary effect of classic equity curve trading

Comparing position-sizing algorithms

Refining your risk budget

Risk amortization

False positives

Order prioritization and trade rejection

Game theory in position sizing

Summary

Risk is a Number

Importing libraries

Interpreting risk

Sharpe ratio: the right mathematical answer to the wrong question

Building a combined risk metric

The Grit Index

Common Sense Ratio

Van Tharp's SQN

Robustness score

Summary

Refining the Investment Universe

Avoiding short selling pitfalls

Liquidity and market impact

Crowded shorts

The fertile ground of high dividend yield

Share buybacks

Fundamental analysis

What do investors really want?

Lessons from the 2007 quants debacle

The Green Hornet complex of the long/short industry

Lessons from Bernie Madoff

Summary

The Long/Short Toolbox

Importing libraries

Gross exposure

Portfolio heat

Portfolio heat bands

Tactical deployment

Step-by-step portfolio heat and exposure management

Net exposure

Net beta

Three reasons why selling futures is the junk food of short-selling

Selling futures is a bet on market cap

Selling futures is a bet on beta

Selling futures is an expensive form of laziness

Concentration

Human limitation

Hedges are not tokens

The paradox of low-volatility returns: structural negative net concentration

Practical tips about concentration

Average number of names

Ratio of big to small bets

Keep your powder dry

Other exposures

Sector exposure

Exchange exposure

Factor exposures

Design your own mandate

Step 1: Strategy formalization

The signal module

The money management module

Step 2: Investment objectives

Step 4: Design your own mandate: product, market, fit

Step 5: Record keeping

Entry

Exits

Position sizing

Journaling

Step 5: Refine your mandate

Summary

Signals and Execution

Importing libraries

Timing is money: the importance of timing orders

Order prioritization

Relative prices and absolute execution

Order types

Exits

Stop loss

Pre-mortem

The Zeigarnik effect

Profitable exits

Entry

Rollover: the aikido of bear market rallies

Moving averages

Retracements

Retest

Putting it all together

Summary

Portfolio Management System

Importing libraries

Symptoms of poor portfolio management systems

Ineffective capital allocation

Undermonitored risk detection

High volatility

High correlation

Poor exposure management

Your portfolio management system is your Iron Man suit

Clarity: bypass the left brain

Relevance: the Iron Man auto radio effect

Simplicity: complexity is a form of laziness

Flexibility: information does not translate into decision

Automating the boring stuff

Building a robust portfolio management system

Summary

Appendix: Stock Screening

Import libraries

Define functions

Control panel

Data download and processing

Heatmaps

Individual process

Other Books You May Enjoy

Index

Landmarks

Cover

Index

Preface

"There is nothing more powerful than an idea whose time has come."

– Victor Hugo

Market participants always want industries to become more efficient: "cut the middle man," "cost-reduction," "rationalization."  We are finally getting a taste of our own medicine. Markets average long-term returns of 8% per annum. Yet, roughly 60% of professional fund managers underperform their benchmark, year in year out. 90% of retail investors blow up. The way we trade has clearly not been working. Despite all the bravado, the emperors of money have been parading naked. We collectively need to evolve if we want to survive this market Darwinism. Evolution does not take prisoners.

Global warming is a reality in the financial services. The glacier of actively managed money is melting. Mutual funds face intense pressure from exchange traded funds to lower fees. Fortunately, there has been a solution right under our noses all along, a terra incognita where mankind has never set foot.

If we were to stack all books about investing, trading, markets, on top of each other, trips to the moon would be a sad ecological reality. Yet, if we were to line up books about short selling side by side on a dinner table, there would still be enough room for a bottle of Côte-Rôtie, a divine northern Rhône valley Shiraz-Viognier wine, and a few glasses. Short selling is the key to raising and maintaining assets under management. When the markets tank, those who still stand up, stand out. Money may temporarily flow (and ebb) to those who shine in bull markets, but it will always gravitate towards those who perform in down markets. Investors may forget unimpressive returns, yet they will not forgive drawdowns.

Short selling commands premium fees. Suppose you add a short book to your endangered long-only mutual fund. From that day on, you can command premium management fees and even demand steep performance fees. You will enjoy more freedom in your mandate to trade exotic instruments, freedom to keep a higher cash balance, freedom to selectively disclose your positions. And the price of freedom is to learn to sell short.

Who this book is for

This book is written by a practitioner for practitioners. It is for advanced to expert market participants. Even if you have never coded a line in Python, this book is still for you. It was originally written without the source code. This later addition is meant to help readers implement the concepts in real life. If you are an experienced coder but new to the markets, you will pick up concepts that will help you on your journey. You may however want to supplement your market education with further reading.

Even if you choose never to sell short, this book is still for you. The tools and techniques developed for the short side are built to withstand extreme conditions. If you can survive the arid environment of the short side, imagine how you will thrive on the long side. If you are in the long/short business, the question is not whether you should read this book or not. The real question is can you afford to not read this book. You may disagree with some ideas, but they will provoke thoughts and spark conversation. The ideas we originally resist are the ones that makes us grow, so welcome to the space beyond your comfort zone.

What this book covers

Part I,The Inner Game: Demystifying Short Selling

Chapter 1, The Stock Market Game, discusses a few questions: "Is the stock market an art or a science? What if it was just a game? How do you win an infinite complex random game?" This chapters sets the context of the rest of the book.

Chapter 2, 10 Classic Myths About Short Selling, dispels enduring myths about short selling. The most important question is: "do you want to retire on numbers or stories?" If the former, then short sellers are your pension's best friend.

Chapter 3, Take a Walk on the Wild Short Side, explains the arc of the long side mindset on the short side and its predictable failure. This chapter describes the three endemic challenges of the short side: market dynamics, scarcity mentality, and information asymmetry.

Part II,The Outer Game: Developing a Robust Trading Edge

Chapter 4, Long/Short Methodologies: Absolute and Relative, addresses idea generation. You will be able to consistently generate as many if not more ideas on the short side than on the long side.

Chapter 5, Regime Definition, explains several regime definition methodologies to reclassify stocks as bullish, bearish, or inconclusive.

Chapter 6, The Trading Edge is a Number, and Here is the Formula, aims to demystify the mythical, mystical, magical trading edge. Regardless of the asset class and timeframes, there are only two strategies. We explain the pros and cons of each one.

Chapter 7, Improve Your Trading Edge, outlines seven ways to improve the distribution of returns and build a robust trading edge.

Chapter 8, Position Sizing: Money is Made in the Money Management Module, proves that money is made in the money management module. We introduce a game changing approach to equity curve trading.

Chapter 9, Risk is a Number, introduces four risk metrics that unapologetically measure robustness. Short sellers are exceptional risk managers.

Part III, The Long/Short Game: Building a Long/Short Product

Chapter 10, Refining the Investment Universe, explains some common pitfalls to avoid, and investors' desires to address, in order to help distill a large population of stocks into an investable universe. This chapter paves the way to the final part of the book.

Chapter 11, The Long/Short Toolbox, dives into the four most important levers to manage a long/short portfolio. Now that we know what clients want, we look at the tools available to achieve those objectives.

Chapter 12, Signals and Execution, brings together concepts covered in previous chapters, and goes through signal processing, execution, and other vital components when constructing a long/short investment product.

Chapter 13, Portfolio Management System, looks at one of the most underrated tools in your arsenal. Now that you have added a relative short book, whatever tools you have been using so far are in dire need of a radical upgrade. This chapter goes over topics which will help when designing your own Portfolio Management System.

Appendix, Stock Screening, provides a stock screener tool that will address idea generation, the most pressing issue for market participants, and allow you to put everything you have learned into practice.

To get the most out of this book

Sometimes we win, sometimes we learn. The best disposition to get the maximum out of this book is to have lost money on the markets. This will put you in an open state of mind!

Intermediate knowledge of Python, specifically the use of numpy, pandas, and matplotlib will suffice. We will also use some non-standard Python libraries; yfinance and scipy. High school level competence in algebra and statistics is also necessary.

Download the example code files

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Algorithmic-Short-Selling-with-Python. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781801815192_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example; "From the rolling_profits and rolling_losses functions, calculate profit_ratio."

A block of code is set as follows:

# Import Librariesimport pandas as pd import numpy as np import yfinance as yf %matplotlib inline import matplotlib.pyplot as plt

Any command-line input or output is written as follows:

3.52

Bold: Indicates a new term, an important word, or words that you see on the screen, for example, in menus or dialog boxes, also appear in the text like this. For example: "Did the price beat the volume at weighted average price (VWAP) or not?"

Warnings or important notes appear like this.

Tips and tricks appear like this.

Get in touch

Feedback from our readers is always welcome.

General feedback: Email [email protected], and mention the book's title in the subject of your message. If you have questions about any aspect of this book, please email us at [email protected].

Errata: Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you have found a mistake in this book we would be grateful if you would report this to us. Please visit, http://www.packtpub.com/submit-errata, selecting your book, clicking on the Errata Submission Form link, and entering the details.

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1

The Stock Market Game

"Infinite games have infinite time horizons. And because there is no finish line, no practical end to the game, there is no such thing as "winning" an infinite game. In an infinite game, the objective is to keep playing, to perpetuate the game."

– Simon Sinek

The financial services industry is facing a severe existential crisis. The only things melting faster than the polar ice caps are assets under active management. Evolution does not take prisoners. If active managers do not want to go join the bluefin tuna on the list of endangered species, then maybe learning to sell short would be an invaluable skill to add to their arsenal. As the global financial crisis of 2007-2008 showed us, it's crucial for market participants to be capable of generating profits not only in bull but also in bear markets. To that end, this book will cover the ins and outs of short selling, and develop algorithmic strategies to maximize its effectiveness, with the end goal of creating a robust investment product that will set you apart from your market competitors.

This chapter sets the stage for the book. At some point in your career, you have probably wondered whether the market was more of a science or an art form. What if the market was a perpetual unsolvable puzzle? How do you win an infinite, complex, random game?

We will cover the following topics:

Is the stock market art or science?How do you win this complex, infinite, random game?Playing the short selling game

Is the stock market art or science?

"When bankers get together for dinner, they discuss art. When artists get together for dinner, they discuss money."

– Oscar Wilde

Once upon a time, Lorenzo de Medici praised Michelangelo for the quality of his craftsmanship. Il Divino replied to il Magnifico, "it appears as art only to those who have not worked hard enough to see the craft."

Every market participant has wondered whether the stock market was more of an art than science. The assumption behind art is the notion of innate talent. Some naturals are born gifted. Some aren't, and I am one of those. If talent is innate, then we mere mortals have to resign ourselves that we simply do not have it. However, talent is often an excuse for laziness. Michael Jordan was not a natural. He was thrown out of his basketball team, so he trained and would not go home until he landed 100 free throws. Landed 98? Oops. Do it again. This way, skills can be developed. The output might look like effortless grace. Yet, it takes craft, hard work, perseverance, and something Angela Duckworth calls "grit."

Making money on the markets is not art. It is a skill. In the early 80s, Richard Dennis and William Eckhardt assembled a team, including a poker player, a drug dealer, and other people from all walks of life. They were given a system, starting capital, and sent off to trade futures. Decades later, some of these people still trade. Were they talented? Maybe some of them had some predisposition, but it did not matter. They worked on and at a system, the result of which might have looked like art.

Scientists like to explain the world with definitive formulas. This approach works well for simple and even complicated systems (which can usually be broken down into several simple systems) but not for complex systems:

Simple system: how much fuel do you need to send a rocket to Mars? Complicated system: how do you send someone to Mars? (This can be broken down into simple systems, such as fuel consumption.)Complex system: how do you sustain life on Mars?

Markets are complex systems. Unlike complicated systems, complex ones cannot be broken down into a series of simple systems. The moment you think you have a definitive formula that explains stock prices, ceteris paribus, the markets will adapt and morph into something else.

The point I'm trying to make is that we do not see things as they are. We see things as we think they are. Context filters our perception. If we think something is going to be hard, it is probably not going to be easy.

If we think the stock market is an art, we will marvel at the masterpiece but fail to appreciate the craft. If we think of it as a science, we will look for a definitive formula, only to be fooled by randomness time and again. If we see it as a game, then the child in us will play.

How do you win this complex, infinite, random game?

"There are known knowns, things we know that we know; and there are known unknowns, things that we know we don't know. But there are also unknown unknowns, things we do not know we don't know."

– Donald Rumsfeld

Share prices may reflect fundamentals over time, but the journey is likely to be a random walk. The random walk theory was popularized by Burton Malkiel in A Random Walk Down Wall Street. It essentially postulates that every financial asset has an intrinsic value, yet market prices are hard to accurately predict. Randomness routinely throws market participants off. When even the best of the best in the business succeed roughly 50% of the time, the only conclusion is that randomness cannot be eradicated.

There are two types of games: finite and infinite. A finite game has a clear set of rules, participants, a beginning, a middle, and an end. An infinite game has no set of rules, no beginning, and no end. The objective of a finite game is to win the game. The objective of an infinite game is to stay in the game.

Let's illustrate this with an example. A professional poker player meets a professional trader. The trader plays risky hands throughout the night and wins the game. The next day, the poker player buys a stock the trader recommended. The trader stops out the trade two weeks later, while the gambler forgets about it and doubles his money over the next 3 years. For the trader, poker is a hobby, and he won the poker night because he knew he could afford more risk. Meanwhile, the poker player took calculated risks. He accepted the short-term loss as part of winning the long-term game. When the poker player followed the investment tip, he rode it through the ups and downs, as he was merely using a disposable asset. On the other hand, when the trader closed the same stock and missed the ensuing rally, he was executing risk management.

For the trader, the poker night was a finite game. On the other hand, the stock tip was a finite game for the poker player. They both could afford a higher risk tolerance in each other's games because they knew the game was finite. However, when a game turns from a hobby to a livelihood, we become more risk-averse.

Jack Schwager, best-selling author of the Market Wizards series, often says that no sane person would buy a book on surgery, read it over the weekend, and believe they would be ready to operate on someone's head by Monday. Yet, people buy books on investment, subscribe to a couple of newsletters, and think it is perfectly reasonable to start trading by Monday. It may work for amateurs with a very small sample. After all, there is a 50-50 chance of winning. The same randomness that favors the amateurs hurts the pros who have a much larger sample. The game becomes infinite the moment a hobby turns into work. The gambler may have budgeted for a few bad poker nights a year. Similarly, the trader follows a tight risk management policy. Poker players and star traders have one thing in common: they go to work; it is not supposed to be fun.

This leads us to the central question of this book: how do you beat an infinite complex random game?

How do you win an infinite game?

If you are in an infinite game, you don't win by winning one game or all the games. You win by staying in the game. You win some, you lose some, but you get to stay in the game as long as your average wins multiplied by your win rate exceeds your average loss multiplied by your loss rate. You win as long as your gain expectancy stays positive. Your job as a stock picker, trader, investor, speculator, or whatever you choose to call yourself, is to maximize that gain expectancy. That is the part where, out of all the stocks you picked, the ones you keep need to look good, the result of which may eventually look like art. This is what we are going to work on in Part II, The Outer Game: Developing a Robust Trading Edge, so keep reading, Michelangelo.

How do you beat complexity?

When faced with a complex problem, we intuitively believe the solution must be complicated. Not always. The trajectory of a fast projectile is rocket science, quite literally. Now, when was the last time you saw Serena Williams solving stochastic equations by the side of the court? This is called the gaze heuristic: see, run, intercept, repeat. Complex problems have simple solutions.

Many quantitative traders, affectionately referred to as quants, believe they have to justify their PhDs with convoluted equations. Proof by mathematical intimidation undoubtedly strokes the ego, and yet a high IQ does not rhyme with high performance. The stock market is the place where Nobel prize winners go to get humbled.

On the other hand, it appears there is a simple heuristic hiding in plain sight that beats the complexity of the market. This simple mantra is: "cut your losers, run your winners." Part II, The Outer Game: Developing a Robust Trading Edge, will give practical techniques to reduce the drag of losers.

How do you beat randomness?

As a species, our survival has depended on how we deal with randomness. The same survival mechanism we instinctively apply in daily life does not transfer to the markets. Understanding randomness is critical to the development of a healthy short selling practice. First, let us look at how we approach randomness in the markets. Second, let us look at how we deal with randomness in real life. Third, we will see how we can apply this skill to the markets.

Let us say we design a system to pick stocks. When we build a strategy, we start with some assumptions. If stocks meet certain expectations [insert laundry list of criteria here…], we go long or short. In theory, rich valuations, far above reasonable market expectations, revert to "fair," fair valuation being the price some market participants are willing to pay for the value they perceive. In theory, bad businesses are expected to go bust. In theory, overbought stocks are expected to revert to the mean and vice versa for oversold issues. In theory, this should work. Now, it is time to take the idea for a spin. Randomness can be summarized in the outcome matrix below:

Figure 1.1: Figurative matrix outcome

True positives are when outcomes match expectations. True negatives occur when stocks did not pass our test and went on to exhibit poor performance as predicted. This is when theory has its first encounter with reality. In theory, markets are efficient: all publicly available information should be reflected in the price immediately. In practice, this is not always the case.

Back to the drawing board, the presence of false positives, when outcomes do not match expectations (for example, stocks passed our tests but flopped in practice), suggests we have missed something. In practice, valuations can get and remain rich longer than clients will stay invested. In practice, overbought and oversold technical indicators are signs of sustained strength and weakness, respectively. They indicate the continuation of a trend rather than a reversion to the mean. We are confused and frustrated. Our natural inclination is to refine our thesis, adding layers of complexity to reduce false positives. This approach generates fewer signals, yet false positives do not disappear entirely.

A side effect and classic pitfall for intermediate short sellers of over-filtering are false negatives. This is when stocks exhibit desired behavior but go completely undetected as a result of our more stringent tests. A real-life analogy is dating by checklist. Sometimes people show up with a long laundry list of unattainable standards and unrealistic expectations. In the same way, market participants reject good enough ideas because of their own self-limiting belief systems all the time. They essentially seek reassurance that their pick will perform as expected by applying superfluous filters, but they fail to see that some of those conditions are mutually exclusive or unrealistic. As a result, they systematically price themselves out of the market and miss all the perfectly fine opportunities passing them by. This explains the bloated size of the false negative circle in Figure 1.1.

Structural/crowded shorts are classic examples of over-filtering. They tick all the bad boxes, except obvious trades are rarely profitable. Conversely, high dividend yield value traps are classic examples of false negatives or blind spots. Those stocks have cheap valuations and dividend support. They do not participate in bull markets. They do not provide adequate support in prolonged bear phases either. They are slow-burning underperformers, relegated to the purgatory of forgotten issues. The bottom line is, despite all best efforts, some stocks still fail to be profitable, on the short and long sides. This is a lot more complex than we originally thought. More confusion. Back to the drawing board again.

Continuing with the dating by checklist scenario, one way to beat randomness is as follows. On paper, a person ticks all the boxes. In practice, big red flags pop up: that person does not laugh at your jokes, hates broccoli, stubbornly refuses to debate Kant's "critique of pure reason" with your goldfish—all the classic important stuff.

In real life, you deal with this seemingly random response by aborting the mission. You don't wait until you are married with a couple of kids in tow, a dead goldfish in a bowl, and a mountain of green vegetables rotting in the fridge to break up. It's the same with the markets. A stock might tick all the boxes, but something unforeseen or overlooked pops up and you bail. When we focus all our energy on stock picking, we try to solve randomness with certainty. Trying harder next time to pick the right stock does not solve randomness. Perfectionism is a form of procrastination. The only way to deal with randomness is to accept our fallibility. The faster we fail, the faster we move on.

Let's illustrate this concept with a practical example. We can all agree that stocks underperforming their benchmark have peaked out relative to the index. Within the population that has hit a ceiling, there are 100% of the future underperformers (which would be our key target for short selling) plus some stocks that will meander sideways and go nowhere until they trend again. There is simply no easy way to discriminate the former from the latter a priori. There are, however, simple techniques to deal with freeloaders a posteriori. The way to beat randomness is not to try and be a better stock picker. The way to beat randomness is to accept that at one point or another, you will pick losers and learn how to deal with them. People see all those great market wizards for the few picks that worked well. They do not look at all the ones that were discarded along the way. We have it backward. We want the medal before the race. Great stock pickers should be judged on what they choose to keep, rather than the less profitable picks they discard along the way.

Playing the short selling game

"Follow me if you want to live."

– Arnold Schwarzenegger, Terminator

The mechanics of short selling are deceptively simple. For example, you sell a stock at 100, buy it back at 90, and pocket the 10. It works in absolute or relative to a benchmark. There is only one additional step that needs to take place before the short sale. Short sellers deliver shares they do not own. So, they borrow those shares from a stock lending desk with their brokerage house first. Once they buy the shares back and close the trade, they return those shares.

Do not let that simplicity fool you. Due to the infinite, complex, random nature of the game that we have considered in this chapter, 90% of market participants fail. Of the remaining 10%, fewer than half will ever engage in short selling. That is the unapologetic reality of the markets.

Our objective is to navigate these challenges and succeed on both sides of the portfolio, despite the complexity. If we travel down the same road as everybody else, we will end up with the same results, minus one standard deviation for good measure.

If virtually everyone fails on the forgiving abundance of the long side, then for you to survive on the merciless aridity of the short side, this book must be intentionally different. This book will take you on a road far less traveled. You might disagree with parts of it, but you will come out transformed. For example, like 100% of the people before you, you will conclude that stock picking is bankrupt. You will also get to see for yourself exactly where the money is generated within the investment process.

Summary

In this chapter, we set the context for the rest of the book. The stock market is neither an art form nor a science. Market wizards are not born, nor do they need to be supremely intelligent. They are forged in the crucible of adversity. The stock market is an infinite, complex, random game. The only way to win this game is to stay in it, by adapting your strategy to the market's infinite, complex, random nature, and to pick stocks and cut losses accordingly. In the coming chapters, we will consider how to incorporate short selling into your trading strategy, and implement techniques to improve your success rate and gain expectancy.

Market participants are generally less comfortable selling short than buying long. This is down to a number of technical factors, but also because of a general fear of the practice, propagated by the number of myths related to short selling. We will discuss and disprove these in the next chapter.

2

10 Classic Myths About Short Selling

Since the 1975 movie Jaws, whenever we get in the water, we all have this instinctive apprehension about what swims beneath. Sharks are unparalleled killing machines. They have a better detection system than the most technologically advanced sonar. They swim faster than speed boats. They have three rows of razor-sharp teeth that continuously regrow. Yet, did you know that deep in the comfort of your home, somewhere in the dark, there is something a thousand times deadlier than any great white? There are, on average, 80 shark attacks every year, mostly exploratory bites and mistaken identity. Meanwhile, falling out of bed carries a far greater probability. Sharks are majestic creatures. If they wanted us dead, we would be. Apparently, they don't like junk food.

Short sellers are like sharks, a little less majestic, but still vastly misunderstood and not as deadly as you might think. You know you have a bit of a reputational issue when your brethren, in allegedly the most reviled industry, would still gladly sharpen pitchforks at the single mention of your profession. In this chapter, we will debunk 10 of the most enduring myths surrounding short selling:

Myth #1: Short sellers destroy pensionsMyth #2: Short sellers destroy companiesMyth #3: Short sellers destroy valueMyth #4: Short sellers are evil speculatorsMyth #5: Short selling has unlimited loss potential but limited profit potentialMyth #6: Short selling increases riskMyth #7: Short selling increases market volatilityMyth #8: Short selling collapses share pricesMyth #9: Short selling is unnecessary during bull marketsMyth #10: The myth of the "structural short"

Myth #1: Short sellers destroy pensions

"Do you believe in God, Monsieur Le Chiffre?"

"I believe in a reasonable rate of return."

– James Bond, Casino Royale

During the Great Financial Crisis (GFC) of 2008, many influential figures encouraged market participants to buy stocks to "shore up the market." They claimed it was "the patriotic thing to do." I grew up in an era when patriots were altruistic individuals who put their lives at risk so that others may have a better future. Somewhere along Wall Street, a patriot became someone who put other people's money at risk in order to guarantee an end-of-year bonus and ego massage.

In the GFC, short sellers did not decimate anyone's pension for a simple reason: pension funds did not allocate to short sellers. Neither did they cause the GFC. Short sellers did not securitize toxic debt or cause the real estate market bubble, nor were they responsible for its collapse. Their crime was to do their homework, take the other side, and profit from the debacle.

Traders make one of two things. Either they make money, or they make excuses. When they make money, performance does the talking. When they don't, they scramble for excuses. Short sellers are the perfect scapegoats. The most vitriolic critics of short sellers are not exactly fund management nobility.

Besides, it might be counter-productive in the long term to blame short sellers for one's misfortunes. There are only two ways you can live your life: as a hero or as a victim. A hero takes responsibility, lives up to the challenges, and triumphs over adversity. A victim will blame others for their failures. The "patriots" who blame short sellers choose the path of the victim. Next time asset allocators decide on their allocation, who do you think they would rather allocate to: heroes or victims?

Now, do short sellers really decimate pensions? The single most important question about your own pension is: "do I want to retire on stories, or do I want to retire on numbers?" The day after your retirement, what will matter to you: all those buzz investment themes or the balance in your bank account?

If you think they are one and the same, then keep churning, and wait for either the handshake and the gold watch, or the tap on the shoulder from the line manager for a life-altering conversation. If you choose to retire on numbers, however, then let's have a look at them.

Nothing illustrates the "story versus numbers" dichotomy better than the active versus passive investing debate. Active management refers to fund managers taking bets away from the benchmark, a practice referred to as active money. Passive investing refers to minimizing the tracking error by mimicking the index. Active managers claim their stock-picking ability and portfolio management skills deliver superior returns. However, the numbers tell a different story. According to S&P's Index versus Active reports, the vast majority of active managers underperform the S&P 500 benchmark on 1, 3, and 5 year-horizons. This means that their cumulative compounded returns are lower than the benchmark. Exchange-Traded Funds, or ETFs, fared better than active managers every year on record, even during the most severe market downturns.

A more detailed report regarding active manager performance compared to the benchmarks can be accessed via S&P's SPIVA reports: https://www.spglobal.com/spdji/en/spiva/#/reports/regions.

The proof is in the pudding. The burden of proof is no longer on ETFs but on active managers to prove that they can deliver more than the index. The debate has gradually shifted from "which manager should we allocate to?" to "remind me again why we should be going with an expensive underperforming benchmark hugger when there is a liquid, better, cheaper alternative?"

Unfortunately, there is more. The penalty for deviating from the benchmark is severe. If managers deviate and outperform, they are knighted as "stock pickers." Yet, when they stray and underperform, they are dubbed as having "high tracking errors." This often leads to redemptions, the kiss of death for fund managers. When the choice is between becoming a hero or keeping your job, self-preservation compels active managers to mirror the benchmark, a practice referred to as closet indexing. This is the famous "no-one ever got fired for holding [insert big safe blue-chip stock here…]" line. If active managers end up mirroring their benchmark, then the "active versus passive" debate is a misnomer. It really comes down to a choice between low-cost indexing via an index fund versus expensive closet indexing via a self-preserving active manager. Either way, you get the same index, but with the latter, you also have to pay a middle-man, called an active manager. As John Boggle, founder of Vanguard, used to say: "in investing, you get what you don't pay for."

That does not mean active management should die an unceremonious death. There are exceptional active managers worth every penny of their management fees. It simply means that the average active mutual fund manager gives active management a bad name. Investors who go down the active management route not only take an equity risk premium. They also take an active management risk premium. The current crisis of active management is nothing but the time-honored routine of the middle man in every industry who realizes that they can no longer delay efficiency.

Next, let's look at what happens during bear markets. Markets have returned between 6.3% and 8% on average. Compounded returns over a century are astronomical. So, in theory, you should stay invested for the long run. Explain that again to the cohorts who retired in 2001 and 2009. Markets have gone down 50% twice in a decade. When the response to "too big to fail" and "too much leverage" was to make them bigger, hyper-leverage, and put the same people responsible for the GFC back in the driver's seat, rest assured that markets are bound to hit "soft patches" again. Long-only active managers may display heroic grace under fire during bear markets but, when your net worth has gone down by 50%, a 1 or 2% outperformance is a rounding error. Two double diamond black slopes in a decade and everyone wants bear market insurance: "What should I buy during a bear market?"

Let us reframe the question. Imagine someone walking up to you and asking: "There is a bull market going on. What should I be short selling?" Pause for a second and then consider your reaction. If selling a bull market does not make any sense, then why would you buy anything during a bear market? There is no safe harbor asset class that will magically rise. The only thing that goes up in a bear market is correlation. There will be ample time to buy at bargain-basement prices once the rain of falling knives stops.

During a bear market, the only market participants who can guarantee a reasonable rate of return are those whose mandate is inversely correlated with the index. These people are short sellers. You do not have to like them. You do not even have to stay invested with them during bull markets. More than any other market participants, they understand the cyclicality of inflows and redemptions. If you choose to retire on numbers, then you owe your pension to consider allocating to short sellers.

This brings us to a counter-intuitive conclusion. If you choose to retire on numbers instead of stories, then passive investing is the way to go on the long side. For capital protection and alpha generation during downturns, investors need to allocate to short sellers. In the active management space, the only market participants who will deliver a reasonable rate of return on your pension are short sellers.

Another counter-intuitive conclusion for practitioners is evolution. Active fund managers face an unprecedented existential crisis. If they want to survive, they need to evolve, adapt, and acquire new skills. Short selling might very well be a rare skill uniquely suited to market participants who are determined to stay relevant.

Myth #2: Short sellers destroy companies

"Round up the usual suspects."

– Capitaine Renaud, Casablanca

When the real estate bubble burst in 2007, what were the short sellers in the risk committee and boards of directors at Lehman Brothers doing? Nothing. They did strictly nothing to prevent or remedy the situation because none of them ever sat on any of those committees. In the history of capitalism, no short seller has ever sat on the board of directors of a company whose stock they were selling short.

We can agree that fundamentals drive share prices in the long run. The driving force is the quality of management. Mark Zuckerberg is the genius behind Facebook. Warren Buffett turned an ailing textile company named Berkshire Hathaway into an industrial conglomerate. Jeff Bezos built Amazon. Steve Jobs 2.0 was the architect of Apple's renaissance. If top management is so eager to take credit for success, then it should equally be held responsible for failure. Steve Jobs 1.0 ran Apple into the ground. Kay Whitmore buried Kodak. The responsibility for Bears Stearns, Lehman Brothers, Merrill Lynch, and AIG falls squarely on the shoulders of those at the helm. Short sellers did not make any of the bad decisions that destroyed those companies. Bad management makes bad decisions that lead to bad outcomes, the same way that good management makes good decisions that lead to good results.

No one captured what happens at the highest echelon of companies better than Steve Jobs in his 1995 Lost Interview. He mentioned that "groupthink" amidst the rarefied atmosphere of top management in venerable institutions sometimes leads to a cognitive bias called the Dunning-Kruger effect. As an example, Kodak was once an iconic brand. Management believed they were so ahead of the game they could indefinitely delay innovation. They took a step backward to their old core technology when the world was moving forward. As tragic as it was back then, there is no flower on Kodak's grave today. The world, and even the 50,000 Kodak employees laid off, have moved on. Today, Kodak is a case study in the failure of management to embrace innovation.

Top management love to surround themselves with bozocrats, non-threatening obedient "yes-men," whose sole purpose is to reassure the upper crust of their brilliance, reinforcing a fatal belief in infallibility. Dissent is squashed. Innovation, branded as cannibalization, is swiftly buried in the Kodak sarcophagus of innovation.

Top management becomes so infatuated with their brilliance that they do not even realize they are detached from reality. Given sufficient time, their obsolete products destroy them from the inside. Short sellers simply ride arrogance to its dusty end.

The history of capitalism is the story of evolution. During World War I, the dominant mode of transportation was the horse. On the first day of World War II, the world woke up in horror to a German panzer division mowing down the Polish cavalry. The cruel lesson is that evolution does not take prisoners. Out of the hundreds of car manufacturers between the two world wars emerged a few winners. The rest of the industry went out of business. For every winner, there are countless losers. Today, everyone is perfectly happy with horse-carts running around Central Park, but no one sheds tears over the defunct horse-cart industry. Everyone has forgotten the names of all those small car manufacturers. The world has moved on.

We often see the S&P 500 as this monolithic hall of corporate fame and power, but we forget that since the index was formed in 1957, only 86 of the original 500 constituents are still in the index. The other 414 have either gone bankrupt or been merged into larger companies. Radio Shack did not go out of business because of short sellers. It went out of business because it could not evolve to face the competition of Amazon and the likes of Best Buy. Short sellers do not destroy companies. They escort obsolescence out of the inexorable march of evolution.

Myth #3: Short sellers destroy value

"Price is what you pay, value is what you get."

– Warren Buffett

Market commentators love to put a dollar sign on the destruction of value every time share prices tank. The net worth of Mr Zuckerberg shrank by $12 billion on July 26, 2018. Since short sellers stand to profit from the drop, they are guilty of value destruction by association. For example, George Soros is often associated with the fall of the British pound in 1995. How could one man single-handedly bring down the currency of one of the wealthiest nations on earth? He bet big on the Bank of England's unsustainable stance.

At the heart of this is a confusion between intrinsic and market values. One is value, the other is valuation. Intrinsic value is the net wealth companies create through the sale of products and services. Market value is the price market participants are willing to pay. Market and intrinsic value live in parallel universes that rarely intersect. One is hard work. The other is the fabled Keynesian beauty contest. The bottom line is that shareholders do not create any more value than short sellers destroy any.

Myth #4: Short sellers are evil speculators

"I am soft, I am lovable. But what I really want to do is reach in, rip out their heart, and eat it before they die."

– Richard Fuld, fallen angel, on short sellers

People often forget that when Steve Jobs returned to the helm, the iconic logo once again made the cover of magazines in a casket. Amazon was not always the darling of Wall Street. What is now considered visionary management was, for a long time, branded as stubborn disrespect for shareholders.

When executives whine about vicious rumors spread by short sellers and their agents, they forget that Steve Jobs and Jeff Bezos had to stomach the same vitriol for years. They came up with the best antidote against short sellers. Make products that sell, manage your company properly, and short sellers will go away. Today, no short seller would ever take a stab at Apple. Note to all CEOs, the vaccine against short sellers is: put the interests of the company, its employees, its customers, the environment, its shareholders, and its management in that order and things will be fine.

If your approach is instead to hollow out resources from R&D, customer service, randomly "restructure" personnel, and flatten the organization chart, to engage in aggressive accounting practices, and vote for ridiculous stock option plans for the board, to gobble up sterile acquisitions and, above all, finance share buy-back plans, then, one day, there will be a pot of gold on the other side of the rainbow to be shared among short sellers.

In 2007, Mathew Rothman, global head of quantitative research, published a note about the demise of quants that went on to become the most circulated piece of research in the history of Lehman Brothers. As the battle between short sellers and Lehman escalated, he showed top management a white paper from Owen Lamont entitled Go Down Fighting: Short Sellers vs. Firms